No GPU automatically detected. Setting SETTINGS.GPU to 0, and SETTINGS.NJOBS to cpu_count.

Data Collection

This section of the notebook contains the functions that query the different datasets. This section is intentionally not included in this output.

Pull Data PIT:

Since data was queried directly from databases, clean samples of each were created and provided separately.

Pull TimeSeries of Data:

Overall Ratings Data Processing

human_capital_theme_weight human_capital_theme_score human_capital_dev_weight human_capital_dev_score human_capital_dev_exp_score human_capital_dev_mgmt_score
count 14235.000000 14235.000000 14235.000000 14235.000000 14235.000000 14235.000000
mean 19.730032 4.595265 10.684370 4.789055 6.700808 4.499747
std 7.752800 2.030963 10.750471 2.274230 1.732765 2.021378
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 15.000000 3.100000 0.000000 3.100000 5.700000 3.100000
50% 20.000000 4.600000 13.000000 4.800000 7.000000 4.500000
75% 25.000000 6.000000 20.000000 6.400000 8.100000 6.200000
max 56.000000 10.000000 35.000000 10.000000 10.000000 10.000000

Iterative Clustering of Data

HCM Data Processing

workforce_part_time_over_20pct_nm women_exec_mgmt_pct_recent women_workforce_pct_recent emp_turnover_annual_pct_recent prof_dev_train_hours_per_emp_recent women_senior_mgmt_pct_recent human_capital_dev_med_risk_bus_pct human_capital_dev_high_risk_bus_pct
count 9762.000000 7108.000000 4641.000000 2650.000000 2318.000000 1237.000000 10917.000000 10917.000000
mean -0.035239 17.205867 34.834044 11.519623 30.897006 20.489491 46.010725 44.745759
std 0.184392 17.426977 18.473922 10.338212 37.585408 16.981514 46.716790 47.220603
min -1.000000 0.000000 0.000000 0.000000 0.030000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 20.800000 5.000000 11.800000 6.300000 0.000000 0.000000
50% 0.000000 14.300000 34.000000 9.000000 21.000000 18.900000 24.310000 13.100000
75% 0.000000 27.300000 48.000000 15.000000 37.247500 33.300000 100.000000 100.000000
max 0.000000 100.000000 100.000000 133.000000 682.030000 100.000000 100.000000 100.000000
['women_exec_mgmt_pct_recent',
 'women_workforce_pct_recent',
 'emp_turnover_annual_pct_recent',
 'prof_dev_train_hours_per_emp_recent',
 'women_senior_mgmt_pct_recent',
 'human_capital_dev_high_risk_bus_pct']
women_exec_mgmt_pct_recent women_workforce_pct_recent emp_turnover_annual_pct_recent prof_dev_train_hours_per_emp_recent women_senior_mgmt_pct_recent human_capital_dev_high_risk_bus_pct
count 7108.000000 4641.000000 2650.000000 2318.000000 1237.000000 10917.000000
mean 17.205867 34.834044 11.519623 30.897006 20.489491 44.745759
std 17.426977 18.473922 10.338212 37.585408 16.981514 47.220603
min 0.000000 0.000000 0.000000 0.030000 0.000000 0.000000
25% 0.000000 20.800000 5.000000 11.800000 6.300000 0.000000
50% 14.300000 34.000000 9.000000 21.000000 18.900000 13.100000
75% 27.300000 48.000000 15.000000 37.247500 33.300000 100.000000
max 100.000000 100.000000 133.000000 682.030000 100.000000 100.000000
women_exec_mgmt_pct_recent women_workforce_pct_recent emp_turnover_annual_pct_recent prof_dev_train_hours_per_emp_recent women_senior_mgmt_pct_recent human_capital_dev_high_risk_bus_pct
count 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000
mean 17.457342 36.549789 10.915612 31.084979 23.013924 41.956667
std 18.066189 18.636729 9.481541 26.832484 15.846943 45.000002
min 0.000000 1.700000 0.000000 1.210000 0.000000 0.000000
25% 0.000000 21.500000 5.000000 13.000000 11.100000 0.000000
50% 14.600000 36.100000 9.000000 23.350000 23.100000 17.210000
75% 28.600000 52.000000 14.000000 40.000000 33.300000 99.990000
max 80.000000 78.500000 70.000000 173.000000 66.900000 100.000000
workforce_part_time_over_20pct_nm women_exec_mgmt_pct_recent women_workforce_pct_recent emp_turnover_annual_pct_recent prof_dev_train_hours_per_emp_recent women_senior_mgmt_pct_recent human_capital_dev_med_risk_bus_pct human_capital_dev_high_risk_bus_pct
count 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000
mean -0.059072 17.457342 36.549789 10.915612 31.084979 23.013924 49.967046 41.956667
std 0.236258 18.066189 18.636729 9.481541 26.832484 15.846943 44.978101 45.000002
min -1.000000 0.000000 1.700000 0.000000 1.210000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 21.500000 5.000000 13.000000 11.100000 0.000000 0.000000
50% 0.000000 14.600000 36.100000 9.000000 23.350000 23.100000 51.110000 17.210000
75% 0.000000 28.600000 52.000000 14.000000 40.000000 33.300000 100.000000 99.990000
max 0.000000 80.000000 78.500000 70.000000 173.000000 66.900000 100.000000 100.000000

Iterative Clustering of Data

Calculate Work Force Sophistication Score

workforce_part_time_over_20pct_nm women_exec_mgmt_pct_recent women_workforce_pct_recent emp_turnover_annual_pct_recent prof_dev_train_hours_per_emp_recent women_senior_mgmt_pct_recent human_capital_dev_med_risk_bus_pct human_capital_dev_high_risk_bus_pct low med high score
count 9762.000000 7108.000000 4641.000000 2650.000000 2318.000000 1237.000000 10917.000000 10917.000000 10917.000000 10917.000000 10917.000000 10917.000000
mean -0.035239 17.205867 34.834044 11.519623 30.897006 20.489491 46.010725 44.745759 9.243517 46.010725 44.745759 67.751121
std 0.184392 17.426977 18.473922 10.338212 37.585408 16.981514 46.716790 47.220603 27.032061 46.716790 47.220603 30.571952
min -1.000000 0.000000 0.000000 0.000000 0.030000 0.000000 0.000000 0.000000 -0.010000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 20.800000 5.000000 11.800000 6.300000 0.000000 0.000000 0.000000 0.000000 0.000000 50.000000
50% 0.000000 14.300000 34.000000 9.000000 21.000000 18.900000 24.310000 13.100000 0.000000 24.310000 13.100000 55.080000
75% 0.000000 27.300000 48.000000 15.000000 37.247500 33.300000 100.000000 100.000000 0.000000 100.000000 100.000000 100.000000
max 0.000000 100.000000 100.000000 133.000000 682.030000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000
invalid value encountered in greater_equal
invalid value encountered in less_equal
workforce_part_time_over_20pct_nm women_exec_mgmt_pct_recent women_workforce_pct_recent emp_turnover_annual_pct_recent prof_dev_train_hours_per_emp_recent women_senior_mgmt_pct_recent human_capital_dev_med_risk_bus_pct human_capital_dev_high_risk_bus_pct low med high score
count 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000
mean -0.059072 17.457342 36.549789 10.915612 31.084979 23.013924 49.967046 41.956667 8.076287 49.967046 41.956667 66.940190
std 0.236258 18.066189 18.636729 9.481541 26.832484 15.846943 44.978101 45.000002 24.979332 44.978101 45.000002 28.613392
min -1.000000 0.000000 1.700000 0.000000 1.210000 0.000000 0.000000 0.000000 -0.010000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 21.500000 5.000000 13.000000 11.100000 0.000000 0.000000 0.000000 0.000000 0.000000 50.000000
50% 0.000000 14.600000 36.100000 9.000000 23.350000 23.100000 51.110000 17.210000 0.000000 51.110000 17.210000 58.605000
75% 0.000000 28.600000 52.000000 14.000000 40.000000 33.300000 100.000000 99.990000 0.010000 100.000000 99.990000 99.990000
max 0.000000 80.000000 78.500000 70.000000 173.000000 66.900000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
Cluster_No_WF 0 1 2
women_exec_mgmt_pct_recent 9.121296 25.348962 20.230435
women_workforce_pct_recent 21.203704 50.070755 46.295652
emp_turnover_annual_pct_recent 9.111111 12.518868 12.000000
prof_dev_train_hours_per_emp_recent 22.691019 25.649340 95.551304
women_senior_mgmt_pct_recent 13.371296 31.395283 29.665217
score 65.654028 68.320660 66.617391
Cluster 0 1 2 3 4 5
women_exec_mgmt_pct_recent 6.849254 23.033333 11.555556 14.377500 34.359259 41.946154
women_workforce_pct_recent 17.562687 45.994444 35.493651 57.016667 43.929630 45.496154
emp_turnover_annual_pct_recent 9.776119 12.222222 8.301587 12.027778 15.592593 12.884615
prof_dev_train_hours_per_emp_recent 20.613284 103.792778 26.686825 38.863611 15.166296 24.151154
women_senior_mgmt_pct_recent 10.283582 29.444444 23.760317 27.133333 35.033333 31.373077
score 51.053284 67.566111 96.370000 52.551528 28.246667 96.240000
Clustering with Workforce Sophistication Score:

Percent of Variance Explained:
PCA Dimension 1:  33.14%
PCA Dimension 2:  30.12%
PCA Dimension 3:  17.96%
PCA Dimension 4:  10.02%
PCA Dimension 5:  5.66%
PCA Dimension 6:  3.1%

Total % of Variance Explained: 100.0%
0_Component Loadings:
women_exec_mgmt_pct_recent:  0.1433
women_workforce_pct_recent:  0.1957
emp_turnover_annual_pct_recent:  -0.0363
prof_dev_train_hours_per_emp_recent:  0.5078
women_senior_mgmt_pct_recent:  0.1201
score:  0.817
 
1_Component Loadings:
women_exec_mgmt_pct_recent:  0.1901
women_workforce_pct_recent:  0.2728
emp_turnover_annual_pct_recent:  0.0389
prof_dev_train_hours_per_emp_recent:  0.7249
women_senior_mgmt_pct_recent:  0.1811
score:  -0.5742
 
2_Component Loadings:
women_exec_mgmt_pct_recent:  0.4883
women_workforce_pct_recent:  0.5441
emp_turnover_annual_pct_recent:  0.1166
prof_dev_train_hours_per_emp_recent:  -0.4596
women_senior_mgmt_pct_recent:  0.4907
score:  0.0027
 
Clustering with NO Workforce Sophistication Score:

Percent of Variance Explained:
PCA Dimension 1:  45.81%
PCA Dimension 2:  26.43%
PCA Dimension 3:  14.75%

Total % of Variance Explained: 87.0%
0_Component Loadings:
women_exec_mgmt_pct_recent:  0.2384
women_workforce_pct_recent:  0.3358
emp_turnover_annual_pct_recent:  0.0083
prof_dev_train_hours_per_emp_recent:  0.8851
women_senior_mgmt_pct_recent:  0.2168
 
1_Component Loadings:
women_exec_mgmt_pct_recent:  0.4883
women_workforce_pct_recent:  0.5442
emp_turnover_annual_pct_recent:  0.1169
prof_dev_train_hours_per_emp_recent:  -0.4593
women_senior_mgmt_pct_recent:  0.4908
 
2_Component Loadings:
women_exec_mgmt_pct_recent:  0.794
women_workforce_pct_recent:  -0.5846
emp_turnover_annual_pct_recent:  -0.1433
prof_dev_train_hours_per_emp_recent:  0.0289
women_senior_mgmt_pct_recent:  -0.0804
 
workforce_part_time_over_20pct_nm women_exec_mgmt_pct_recent women_workforce_pct_recent emp_turnover_annual_pct_recent prof_dev_train_hours_per_emp_recent women_senior_mgmt_pct_recent human_capital_dev_med_risk_bus_pct human_capital_dev_high_risk_bus_pct low med high score Cluster_No_WF Cluster human_capital_theme_weight human_capital_theme_score human_capital_dev_weight human_capital_dev_score human_capital_dev_exp_score human_capital_dev_mgmt_score
count 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000 225.000000 225.000000 225.000000 225.000000 225.000000 225.000000
mean -0.059072 17.457342 36.549789 10.915612 31.084979 23.013924 49.967046 41.956667 8.076287 49.967046 41.956667 66.940190 0.641350 2.067511 20.102222 5.235111 9.373333 5.672444 6.735111 5.451556
std 0.236258 18.066189 18.636729 9.481541 26.832484 15.846943 44.978101 45.000002 24.979332 44.978101 45.000002 28.613392 0.652618 1.676002 8.419431 2.043099 10.379977 2.123812 1.795198 1.881633
min -1.000000 0.000000 1.700000 0.000000 1.210000 0.000000 0.000000 0.000000 -0.010000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.500000 0.000000 0.000000 2.300000 0.700000
25% 0.000000 0.000000 21.500000 5.000000 13.000000 11.100000 0.000000 0.000000 0.000000 0.000000 0.000000 50.000000 0.000000 0.000000 15.000000 3.800000 0.000000 4.300000 5.900000 4.200000
50% 0.000000 14.600000 36.100000 9.000000 23.350000 23.100000 51.110000 17.210000 0.000000 51.110000 17.210000 58.605000 1.000000 2.000000 20.000000 5.200000 0.000000 5.700000 7.100000 5.600000
75% 0.000000 28.600000 52.000000 14.000000 40.000000 33.300000 100.000000 99.990000 0.010000 100.000000 99.990000 99.990000 1.000000 3.000000 25.000000 6.600000 17.000000 6.900000 8.000000 7.000000
max 0.000000 80.000000 78.500000 70.000000 173.000000 66.900000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 2.000000 5.000000 56.000000 10.000000 35.000000 10.000000 10.000000 9.300000
Cluster 0 1 2 3 4 5
workforce_part_time_over_20pct_nm -0.059701 0.000000 -0.031746 -0.111111 -0.148148 0.000000
women_exec_mgmt_pct_recent 6.849254 23.033333 11.555556 14.377500 34.359259 41.946154
women_workforce_pct_recent 17.562687 45.994444 35.493651 57.016667 43.929630 45.496154
emp_turnover_annual_pct_recent 9.776119 12.222222 8.301587 12.027778 15.592593 12.884615
prof_dev_train_hours_per_emp_recent 20.613284 103.792778 26.686825 38.863611 15.166296 24.151154
women_senior_mgmt_pct_recent 10.283582 29.444444 23.760317 27.133333 35.033333 31.373077
human_capital_dev_med_risk_bus_pct 90.021791 31.810000 6.345079 86.797500 56.229630 7.519231
human_capital_dev_high_risk_bus_pct 6.042388 51.661111 93.197460 9.152778 0.131852 92.480385
low 3.935821 16.528889 0.457460 4.049722 43.638519 0.000385
med 90.021791 31.810000 6.345079 86.797500 56.229630 7.519231
high 6.042388 51.661111 93.197460 9.152778 0.131852 92.480385
score 51.053284 67.566111 96.370000 52.551528 28.246667 96.240000
Cluster_No_WF 0.029851 2.000000 0.396825 1.083333 0.851852 1.038462
human_capital_theme_weight 22.301587 21.812500 19.885246 16.138889 18.083333 21.640000
human_capital_theme_score 5.411111 4.543750 5.101639 5.997222 5.320833 4.380000
human_capital_dev_weight 2.380952 12.500000 11.786885 12.555556 3.875000 19.800000
human_capital_dev_score 6.003175 5.631250 4.885246 6.194444 7.337500 4.436000
human_capital_dev_exp_score 5.712698 6.793750 8.080328 6.638889 4.450000 8.324000
human_capital_dev_mgmt_score 4.796825 5.425000 5.963934 5.838889 4.983333 5.760000
Cluster_No_WF 0 1 2
workforce_part_time_over_20pct_nm -0.064815 -0.066038 0.000000
women_exec_mgmt_pct_recent 9.121296 25.348962 20.230435
women_workforce_pct_recent 21.203704 50.070755 46.295652
emp_turnover_annual_pct_recent 9.111111 12.518868 12.000000
prof_dev_train_hours_per_emp_recent 22.691019 25.649340 95.551304
women_senior_mgmt_pct_recent 13.371296 31.395283 29.665217
human_capital_dev_med_risk_bus_pct 58.365278 44.923962 33.773913
human_capital_dev_high_risk_bus_pct 36.471389 45.858679 49.730435
low 5.163333 9.217358 16.495652
med 58.365278 44.923962 33.773913
high 36.471389 45.858679 49.730435
score 65.654028 68.320660 66.617391
Cluster 0.870370 3.415094 1.478261
human_capital_theme_weight 21.346535 18.291262 23.000000
human_capital_theme_score 5.254455 5.327184 4.690476
human_capital_dev_weight 5.920792 12.291262 11.666667
human_capital_dev_score 5.642574 5.711650 5.623810
human_capital_dev_exp_score 6.523762 6.958252 6.657143
human_capital_dev_mgmt_score 5.216832 5.716505 5.280952
invalid value encountered in greater_equal
invalid value encountered in less_equal
invalid value encountered in log
ML Score ML Rating PctTile Z Score Z Score Centered Z Score Norm human_capital_theme_weight human_capital_theme_score human_capital_dev_weight human_capital_dev_score human_capital_dev_exp_score human_capital_dev_mgmt_score
count 327.000000 327.000000 3.270000e+02 327.000000 327.000000 301.000000 301.000000 301.000000 301.000000 301.000000 301.000000
mean 43.175941 5.015291 3.228814e-16 4.996594 5.021055 20.169435 5.137209 9.073090 5.597342 6.634884 5.265116
std 17.761754 2.891162 1.001533e+00 0.982389 2.007588 8.546024 1.984879 10.368284 2.061971 1.758222 1.823096
min 1.361333 0.030581 -2.357802e+00 2.642198 0.000000 0.000000 0.300000 0.000000 0.000000 2.300000 0.700000
25% 30.121333 2.522936 -7.361106e-01 4.263889 3.459567 15.000000 3.800000 0.000000 4.300000 5.700000 4.100000
50% 41.909333 5.015291 -7.142024e-02 4.928580 4.913046 20.000000 5.100000 0.000000 5.500000 6.600000 5.300000
75% 54.615333 7.507645 6.450334e-01 5.645033 6.479717 25.000000 6.400000 15.000000 6.800000 7.800000 6.700000
max 151.600000 10.000000 6.113711e+00 10.000000 10.000000 56.000000 10.000000 35.000000 10.000000 10.000000 9.300000
ML Score ML Rating PctTile Z Score Z Score Centered Z Score Norm human_capital_theme_weight human_capital_theme_score human_capital_dev_weight human_capital_dev_score human_capital_dev_exp_score human_capital_dev_mgmt_score
count 301.000000 301.000000 301.000000 301.000000 301.000000 301.000000 301.000000 301.000000 301.000000 301.000000 301.000000
mean 43.574230 5.068325 0.022458 5.018758 5.065716 20.169435 5.137209 9.073090 5.597342 6.634884 5.265116
std 17.496269 2.839752 0.986563 0.965505 1.956938 8.546024 1.984879 10.368284 2.061971 1.758222 1.823096
min 6.073333 0.061162 -2.092106 2.907894 0.494409 0.000000 0.300000 0.000000 0.000000 2.300000 0.700000
25% 30.732000 2.629969 -0.701677 4.298323 3.534863 15.000000 3.800000 0.000000 4.300000 5.700000 4.100000
50% 41.977333 5.045872 -0.067586 4.932414 4.921431 20.000000 5.100000 0.000000 5.500000 6.600000 5.300000
75% 54.493333 7.492355 0.638154 5.638154 6.464674 25.000000 6.400000 15.000000 6.800000 7.800000 6.700000
max 151.600000 10.000000 6.113711 10.000000 10.000000 56.000000 10.000000 35.000000 10.000000 10.000000 9.300000

Interaction with Policy Data

Possible Values for Degree Program Policy:
""
"Programs covering all employees (including part-time and contractors)"
"No evidence"
"Programs covering all permanent employees (excluding part-time and contractors)"
"General statements on training and development"
Encoding of Possible Values for Degree Program Policy: 

: 	 0
Programs covering all employees (including part-time and contractors): 	 1
No evidence: 	 -1
General statements on training and development: 	 0
Programs covering all permanent employees (excluding part-time and contractors): 	 1
Possible Values for Degree Program Policy:
""
"General statements on leadership training with unknown scope or achieved results"
"Comprehensive succession planning  & development programs at multiple levels"
"No evidence"
"Programs focusing on internal upward mobility through training and development"
"Minimum practices expected based on domestic industry norms"
Encoding of Possible Values for Degree Program Policy: 

: 	 0
General statements on leadership training with unknown scope or achieved results: 	 0
Comprehensive succession planning  & development programs at multiple levels: 	 1
No evidence: 	 -1
Minimum practices expected based on domestic industry norms: 	 0
Programs focusing on internal upward mobility through training and development: 	 1
Degree Program Dev Program Overall Policy workforce_part_time_over_20pct_nm women_exec_mgmt_pct_recent women_workforce_pct_recent emp_turnover_annual_pct_recent prof_dev_train_hours_per_emp_recent women_senior_mgmt_pct_recent human_capital_dev_med_risk_bus_pct ... score ML Score ML Score with Policy ML Score NO Policy human_capital_theme_weight human_capital_theme_score human_capital_dev_weight human_capital_dev_score human_capital_dev_exp_score human_capital_dev_mgmt_score
count 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000 237.000000 ... 237.000000 237.000000 237.000000 237.000000 225.000000 225.000000 225.000000 225.000000 225.000000 225.000000
mean 0.059072 0.645570 0.352321 -0.059072 17.457342 36.549789 10.915612 31.084979 23.013924 49.967046 ... 66.940190 44.344805 27.657826 44.344805 20.102222 5.235111 9.373333 5.672444 6.735111 5.451556
std 0.593656 0.553218 0.426014 0.236258 18.066189 18.636729 9.481541 26.832484 15.846943 44.978101 ... 28.613392 16.654415 18.026667 16.654415 8.419431 2.043099 10.379977 2.123812 1.795198 1.881633
min -1.000000 -1.000000 -1.000000 -1.000000 0.000000 1.700000 0.000000 1.210000 0.000000 0.000000 ... 0.000000 6.153333 -10.286667 6.153333 0.000000 0.500000 0.000000 0.000000 2.300000 0.700000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 21.500000 5.000000 13.000000 11.100000 0.000000 ... 50.000000 30.822000 15.680000 30.822000 15.000000 3.800000 0.000000 4.300000 5.900000 4.200000
50% 0.000000 1.000000 0.500000 0.000000 14.600000 36.100000 9.000000 23.350000 23.100000 51.110000 ... 58.605000 44.765333 26.671333 44.765333 20.000000 5.200000 0.000000 5.700000 7.100000 5.600000
75% 0.000000 1.000000 0.500000 0.000000 28.600000 52.000000 14.000000 40.000000 33.300000 100.000000 ... 99.990000 55.020000 38.246667 55.020000 25.000000 6.600000 17.000000 6.900000 8.000000 7.000000
max 1.000000 1.000000 1.000000 0.000000 80.000000 78.500000 70.000000 173.000000 66.900000 100.000000 ... 100.000000 117.206000 98.113000 117.206000 56.000000 10.000000 35.000000 10.000000 10.000000 9.300000

8 rows × 24 columns

count mean std min 25% 50% 75% max
Z Score Norm with Policy 70.0 5.65 2.62 0.61 3.68 5.36 8.03 10.0
Z Score Norm NO Policy 70.0 5.45 2.40 0.18 3.62 5.70 7.02 10.0
human_capital_dev_exp_score 70.0 6.91 2.11 2.30 5.93 6.95 8.50 10.0
human_capital_dev_score 70.0 5.75 2.54 0.70 4.05 5.65 7.48 10.0
human_capital_theme_score 70.0 5.53 2.77 0.80 3.42 5.80 7.80 10.0
human_capital_theme_weight 70.0 21.19 6.56 0.00 17.00 23.00 25.00 35.0
count mean std min 25% 50% 75% max
Z Score Norm with Policy 167.0 4.79 1.68 0.61 3.64 4.71 5.88 9.25
Z Score Norm NO Policy 167.0 4.87 1.84 0.88 3.33 4.87 6.28 9.76
human_capital_dev_exp_score 155.0 6.66 1.63 2.30 5.90 7.10 7.80 10.00
human_capital_dev_score 155.0 5.64 1.91 0.00 4.40 5.70 6.80 10.00
human_capital_theme_score 155.0 5.10 1.60 0.50 3.85 5.10 6.30 9.60
human_capital_theme_weight 155.0 19.61 9.11 0.00 15.00 18.00 24.00 56.00

End of Python Jupyter Notebook